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     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import argparse\n",
    "\n",
    "def rle_encode(img):\n",
    "    img = img.flatten()\n",
    "    label_dict = {0:\"\"}\n",
    "    for id in range(26,35):\n",
    "        label_dict[id] = \"\"\n",
    "    preid = img[0]\n",
    "    id_num = 1\n",
    "    begin_idx = 0\n",
    "    for pixel_idx in range(1,img.shape[0]):\n",
    "        pixel = img[pixel_idx]\n",
    "        if preid == pixel:\n",
    "            id_num += 1\n",
    "        else:\n",
    "            label_dict[preid] = label_dict[preid] + \"{} {}|\".format(begin_idx,id_num)\n",
    "            begin_idx = pixel_idx\n",
    "            id_num = 1\n",
    "        preid = pixel\n",
    "    return label_dict\n",
    "\n",
    "# result_txt='./docs/result.txt'\n",
    "result_txt='C:/Users/ZZK/Desktop/15.txt'\n",
    "output_path='./docs/submit.csv'\n",
    "\n",
    "def main():\n",
    "    df = pd.DataFrame(columns=['image_id','task','label','pixelcount','result'])\n",
    "    results = open(result_txt,'r').readlines()\n",
    "    pd_idx = 0\n",
    "    for line in results:\n",
    "        result_list = line.strip().split(' ')\n",
    "        img_path,segment_path = result_list[:2]\n",
    "        detection_results = result_list[2:]\n",
    "        for detection_result in detection_results:\n",
    "            result = \",\".join(detection_result.split(',')[:4]) + ',' + detection_result.split(',')[-1]\n",
    "            label = detection_result.split(',')[-2]\n",
    "            df.loc[pd_idx]= img_path,0,label,None,result\n",
    "            pd_idx += 1\n",
    "        print('seg_label:' + segment_path)\n",
    "#         seg_result = cv2.imread('C:/Users/ZZK/auto_drive/docs/seg1/' + segment_path,0)\n",
    "#         label_dict = rle_encode(seg_result)\n",
    "#         for label in range(26,35):\n",
    "#             if label_dict[label] != '':\n",
    "#                 PixelCount = np.where(seg_result==label)[0].shape[0]\n",
    "#                 df.loc[pd_idx]= img_path,1,label,PixelCount,label_dict[label].strip('|')\n",
    "#                 pd_idx += 1\n",
    "\n",
    "    df.to_csv(output_path, index=False)\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_txt='./docs/test_cascade_clean.txt'\n",
    "output_path='C:/Users/ZZK/Desktop/result.txt'\n",
    "\n",
    "results = open(result_txt,'r').readlines()\n",
    "file_to_write = open(output_path, 'w')\n",
    "\n",
    "for line in results:\n",
    "    result_list = line.strip().split(' ')\n",
    "    img_path,segment_path = result_list[:2]\n",
    "    detection_results = result_list[2:]\n",
    "    file_to_write.write(img_path + ' '+segment_path+' ')\n",
    "    for detection_result in detection_results:\n",
    "        label = detection_result.split(',')[0]\n",
    "#         label = str(int(label))\n",
    "        result = \",\".join(detection_result.split(',')[1:5]) + ',' + label +',' + detection_result.split(',')[-1]\n",
    "        file_to_write.write(result +' ')\n",
    "    file_to_write.write('\\n')\n",
    "\n",
    "file_to_write.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "result_txt='./docs/0.1.txt'\n",
    "output_path='C:/Users/ZZK/Desktop/result.txt'\n",
    "\n",
    "results = open(result_txt,'r').readlines()\n",
    "file_to_write = open(output_path, 'w')\n",
    "\n",
    "for line in results:\n",
    "    result_list = line.strip().split(' ')\n",
    "    img_path,segment_path = result_list[:2]\n",
    "    detection_results = result_list[2:]\n",
    "    file_to_write.write(img_path + ' '+segment_path+' ')\n",
    "    for detection_result in detection_results:\n",
    "        label = detection_result.split(',')[-2]\n",
    "        cond = detection_result.split(',')[-1]\n",
    "        x1 = detection_result.split(',')[0]\n",
    "        y1 = detection_result.split(',')[1]\n",
    "        x2 = detection_result.split(',')[2]\n",
    "        y2 = detection_result.split(',')[3]\n",
    "        file_to_write.write(str(int(x1)+random.randint(0,3))+','+str(int(y1)+random.randint(0,3))+','+str(int(x2)+random.randint(0,3))+','+str(int(y2)+random.randint(0,3))+','+label+','+cond)\n",
    "        file_to_write.write(' ')\n",
    "    file_to_write.write('\\n')\n",
    "\n",
    "file_to_write.close()"
   ]
  }
 ],
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   "file_extension": ".py",
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